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Record W3125400747

Producer preferences towards vertical coordination: The case of Canadian beef alliances

2012· preprint· en· W3125400747 on OpenAlexaboutno aff
Bodo Steiner, Kevin Lan, Peter Boxall, Emmanuel Laate, Danyi Yang

Bibliographic record

VenueRePEc: Research Papers in Economics · 2012
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicCooperative Studies and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsRevenueBusinessIncentiveTransaction costYield (engineering)Fed cattleAllianceDatabase transactionCoordination gameIndustrial organizationRevenue sharingMarketingMicroeconomicsEconomicsFinance
DOInot available

Abstract

fetched live from OpenAlex

A survey among cow-calf producers was conducted during 2006 in Western Canada, to assess producers’ preferences towards participation in beef alliances. Producers’ choices were analyzed by varying the degree of vertical coordination in hypothetical lliance participation, while controlling for producer and farm-specific characteristics to explore risk, transaction cost and incentive considerations in participation decisions. Estimates from the attribute-based choice experiments suggest that information sharing regarding animal performance, revenue-risk and residual claimancy are important factors for producers driving alliance choices. Overall, cowcalf producers are willing to move toward higher levels of vertical coordination based on individual animal performance. However, the estimates also suggest that producers consider the benefits from being able to access animal-specific yield and grade data to be smaller than the costs of bearing potentially greater revenue risk as a result of moving towards grid-based pricing, and the transaction costs associated with relationship-building in alliances.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.871
Threshold uncertainty score0.877

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.076
GPT teacher head0.308
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2012
Admission routes1
Has abstractyes

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